Animal Bioscience

Asian Australasian Association of Animal Production Societies (아세아태평양축산학회)
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Biometric identification of Black Bengal goat: unique iris pattern matching system vs deep learning approach

  • Menalsh Laishram (Department of Livestock Production Management, West Bengal University of Animal and Fishery Sciences) ;
  • Satyendra Nath Mandal (Department of Information Technology, Kalyani Government Engineering College) ;
  • Avijit Haldar (ICAR-Agricultural Technology Application Research Institute Kolkata, Indian Council of Agricultural Research) ;
  • Shubhajyoti Das (Department of Information Technology, Kalyani Government Engineering College) ;
  • Santanu Bera (Department of Livestock Production Management, West Bengal University of Animal and Fishery Sciences) ;
  • Rajarshi Samanta (Department of Livestock Production Management, West Bengal University of Animal and Fishery Sciences)
  • Received : 2022.04.18
  • Accepted : 2022.09.26
  • Published : 2023.06.01
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Abstract

Objective: Iris pattern recognition system is well developed and practiced in human, however, there is a scarcity of information on application of iris recognition system in animals at the field conditions where the major challenge is to capture a high-quality iris image from a constantly moving non-cooperative animal even when restrained properly. The aim of the study was to validate and identify Black Bengal goat biometrically to improve animal management in its traceability system. Methods: Forty-nine healthy, disease free, 3 months±6 days old female Black Bengal goats were randomly selected at the farmer's field. Eye images were captured from the left eye of an individual goat at 3, 6, 9, and 12 months of age using a specialized camera made for human iris scanning. iGoat software was used for matching the same individual goats at 3, 6, 9, and 12 months of ages. Resnet152V2 deep learning algorithm was further applied on same image sets to predict matching percentages using only captured eye images without extracting their iris features. Results: The matching threshold computed within and between goats was 55%. The accuracies of template matching of goats at 3, 6, 9, and 12 months of ages were recorded as 81.63%, 90.24%, 44.44%, and 16.66%, respectively. As the accuracies of matching the goats at 9 and 12 months of ages were low and below the minimum threshold matching percentage, this process of iris pattern matching was not acceptable. The validation accuracies of resnet152V2 deep learning model were found 82.49%, 92.68%, 77.17%, and 87.76% for identification of goat at 3, 6, 9, and 12 months of ages, respectively after training the model. Conclusion: This study strongly supported that deep learning method using eye images could be used as a signature for biometric identification of an individual goat.

Keywords

Acknowledgement

The authors would like to express their gratitude to Dr. A. Bandopadhyay, Senior Consultant, ITRA, Ag&Food, Government of India for perceiving the concept of the research work. The authors are thankful to Dr. Manoranjan Roy, Principal Investigator of All India Coordinated Research Project on Goat Improvement and Assistant Professor, Animal Genetics and Breeding, West Bengal University of Animal and Fishery Sciences, Kolkata- 700037, West Bengal, India for extending necessary supports to collect the data at Rangabelia, Gosaba Block, Sunderbans delta, West Bengal, India. The necessary help and cooperation extended by Mr. Kaushik Mukherjee, Mr. Sanket Dan, Mr. Kunal Roy, Mr. Subhranil Mustafi, Mr. Subhojit Roy and Mr. Pritam Ghosh, Department of Information Technology, Kalyani Government Engineering College, Kalyani, Nadia- 741235, West Bengal, India are duly acknowledged.

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